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Semantic Segmentation of Satellite Images using Deep Learning
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A stark increase in the amount of satellite imagery available in recent years has made the interpretation of this data a challenging problem at scale. Deriving useful insights from such images requires a rich understanding of the information present in them. This thesis explores the above problem by designing an automated framework for extracting semantic maps of roads and highways to track urban growth of cities in satellite images. Devising it as a supervised machine learning problem, a deep neural network is designed, implemented and experimentally evaluated. Publicly available datasets and frameworks are used for this purpose. The resulting pipeline includes image pre-processing algorithms that allows it to cope with input images of varying quality, resolution and channels. Additionally, we review a computational graph approach to building a neural network using the TensorFlow framework.

Place, publisher, year, edition, pages
2016.
Keyword [en]
Satellite Imagery, Deep Learning, Semantic Segmentation, Machine Learning, Urban Growth
Identifiers
URN: urn:nbn:se:ltu:diva-38558OAI: oai:DiVA.org:ltu-38558DiVA: diva2:1013270
Educational program
Space Engineering, master's level
Supervisors
Examiners
Available from: 2016-10-04 Created: 2016-10-03 Last updated: 2016-10-04Bibliographically approved

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